A challenge facing higher education institutions (e.g., universities, colleges, etc.) is that there is typically a large number of data models needed to support each institution. Typically, each application used on campus has a uniquely defined data structure with little to no commonality between the various data structures. This results in several complexities.
One complexity is that it tends to be very difficult to have the systems within an institution share information among themselves. Interoperability (i.e., the ability for a process to span more than one system) is virtually impossible without a significant investment in complex technology. Another complexity is that it tends to be nearly impossible to understand which system is the true system of record in terms of analyzing information. As a result of these complexities, the ability for an institution to manage their operations through the use of complex data analysis is currently very limited.
Thus, it would be desirable to provide improved methods of organizing higher education data, and improved higher education data networks.